New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

Aslan, Simge Nur; Özalp, Recep; Uçar, Ayşegül; Güzeliş, Cüneyt

doi:10.1007/s10586-021-03348-7

Cited by 14 publications

(3 citation statements)

References 33 publications

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“…The trained neural network can automatically extract the eigenvalues of the enterprise and output the intelligent manufacturing level according to the evaluation criteria of the same industry. This provides a reference for promoting the development of enterprises (Simge et al, 2021).…”

Section: Research Model and The Methodologymentioning

confidence: 99%

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

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The present work aims to promote the modernization of the real economy and facilitate the manufacturing industry to construct an intelligent manufacturing system. Analysis and evaluation are performed on the intelligent manufacturing system (IMS) of enterprises under the Internet of Things (IoT). First, the connotation and characteristics of machine learning (ML) in the era of IoT are explained, followed by the introduction to neural networks. Second, the optimization process of the neural network is described. Since it has no memory, the traditional deep neural network (DNN) needs to be improved. Then, the Generative Adversarial Network (GAN) is utilized to expand the sample data. Moreover, the confusion matrix is used in the indicator evaluation. The backpropagation neural network (BPNN) is optimized by the genetic algorithm (GA) to improve the network configuration and model performance. In addition, the connotation and development status of IMS are analysed, and the feasibility of applying deep learning to the evaluation of IMS is discussed. Finally, the evaluation model of IMS is designed and built via a neural network, which is ultimately verified in the simulation experiment. The experimental results indicate that the BPNN‐GA algorithm has advantages in predicting fitting accuracy, taking up less memory, and shortening training time; it can achieve generalization and accuracy in the forefront of optimal Pareto value, and the fitting accuracy attains 95.3%. The horizontal evaluation of IMS proves the effectiveness and feasibility of the evaluation model. This shows that the BPNN‐GA algorithm has an excellent evaluation effect on IMS. And the IMS evaluation model is applied to assess the IMS of sample enterprises. The evaluation results demonstrate that there are problems in the current manufacturing industry. For example, the transactions are challenging to be completed smoothly, information technology is updated slowly, and IMS is dependent on collecting resources.

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Section: Research Model and The Methodologymentioning

confidence: 99%

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Yu¹,

Li²,

Li³

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Several studies explain that the more complex a model is, the more vulnerable it will be to reduced accuracy performance [46]. However, some argue that complex models can improve machine learning performance because the function of complexity can solve problems that occur [47]. For example, CNN combined with LSTM will be good because the function of the two models, such as CNN as a model that functions as a recognition skill, LSTM as a model that provides time series data, and the combination serve as a calculation speed in problem-solving [44].…”

Section: Figure 8 Example Of the Graph When Implementing Early Stoppingmentioning

confidence: 99%

Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management

Sunardi,

Yudhana,

Fahmi

2023

RIA

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Waste management, particularly waste sorting, constitutes a critical global challenge. The integration of advanced technology, specifically machine learning, offers potential solutions to this pressing issue. In this study, a convolutional neural network (CNN) model was employed to devise an efficient waste classification system. The model achieved notable results, attaining an accuracy rate of 98.92% and a loss percentage of only 4.03% in overall performance on the test set, utilizing the Kaggle dataset. To further improve the CNN model's performance, advanced preprocessing techniques were implemented alongside a stream lined CNN model, yielding substantial effectiveness. This investigation demonstrates that the application of machine learning techniques can result in highly accurate and efficient waste classification, presenting promising solutions for waste management challenges. By accurately identifying and sorting waste materials, this technology has the potential to significantly reduce the volume of waste directed to landfills, safeguard the environment, and conserve valuable resources.

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“…CNNs [19][20][21] are a deep learning discipline that has proven its success in computer vision and has models designed for various problems. They can be used in vision systems of robots and autonomous vehicles for face [22,23], object [24] and, traffic sign [25,26] recognition. A CNN generally consists of Convolution, Pooling, and Fully Connected Layer structures.…”

Section: Proposed Artificial Intelligence Modelsmentioning

confidence: 99%

Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques

ORHAN,

YAVŞAN

2023

Mathematical Modelling and Numerical Simulation With Applications

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The progressive depletion of the ozone layer poses a significant threat to both human health and the environment. Prolonged exposure to ultraviolet radiation increases the risk of developing skin cancer, particularly melanoma. Early diagnosis and vigilant monitoring play a crucial role in the successful treatment of melanoma. Effective diagnostic strategies need to be implemented to curb the rising incidence of this disease worldwide. In this work, we propose an artificial intelligence-based detection model that employs deep learning techniques to accurately monitor nevi with characteristics that may indicate the presence of melanoma. A comprehensive dataset comprising 8598 images was utilized for the model development. The dataset underwent training, validation, and testing processes, employing the algorithms such as AlexNet, MobileNet, ResNet, VGG16, and VGG19, as documented in current literature. Among these algorithms, the MobileNet model demonstrated superior performance, achieving an accuracy of %84.94 after completing the training and testing phases. Future plans involve integrating this model with a desktop program compatible with various operating systems, thereby establishing a practical detection system. The proposed model has the potential to aid qualified healthcare professionals in the diagnosis of melanoma. Furthermore, we envision the development of a mobile application to facilitate melanoma detection in home environments, providing added convenience and accessibility.

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New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

Cited by 14 publications

References 33 publications

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

RETRACTED: The evaluation of enterprise supply chain intelligent manufacturing system for agricultural interconnection data based on machine learning

Improving Waste Classification Using Convolutional Neural Networks: An Application of Machine Learning for Effective Environmental Management

Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques

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